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hushem_1x_deit_tiny_adamax_lr001_fold1

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metadata
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: hushem_1x_deit_tiny_adamax_lr001_fold1
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: test
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.4444444444444444

hushem_1x_deit_tiny_adamax_lr001_fold1

This model is a fine-tuned version of facebook/deit-tiny-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 1.5661
  • Accuracy: 0.4444

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.001
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.67 1 2.2715 0.2667
No log 2.0 3 2.0269 0.4
No log 2.67 4 1.6111 0.2889
No log 4.0 6 1.4755 0.2444
No log 4.67 7 1.3818 0.4667
No log 6.0 9 1.3523 0.3111
1.6844 6.67 10 1.4010 0.2444
1.6844 8.0 12 1.2634 0.4444
1.6844 8.67 13 1.3983 0.4222
1.6844 10.0 15 1.7897 0.3778
1.6844 10.67 16 1.7305 0.3111
1.6844 12.0 18 1.3560 0.4667
1.6844 12.67 19 1.8545 0.4222
1.001 14.0 21 2.1000 0.3778
1.001 14.67 22 1.2257 0.4889
1.001 16.0 24 1.2741 0.4444
1.001 16.67 25 1.9098 0.3556
1.001 18.0 27 1.4981 0.3778
1.001 18.67 28 1.0949 0.4222
0.7366 20.0 30 1.1640 0.4222
0.7366 20.67 31 1.5156 0.3556
0.7366 22.0 33 1.8559 0.3556
0.7366 22.67 34 1.5735 0.4444
0.7366 24.0 36 1.3202 0.4222
0.7366 24.67 37 1.3837 0.4222
0.7366 26.0 39 1.6707 0.4
0.4908 26.67 40 1.8712 0.3778
0.4908 28.0 42 2.1885 0.3556
0.4908 28.67 43 2.0505 0.3556
0.4908 30.0 45 1.6855 0.4
0.4908 30.67 46 1.5304 0.4222
0.4908 32.0 48 1.5067 0.3778
0.4908 32.67 49 1.5442 0.4222
0.3287 33.33 50 1.5661 0.4444

Framework versions

  • Transformers 4.35.0
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.6
  • Tokenizers 0.14.1